1. Field
Exemplary embodiments of the present disclosure relate to a neuromorphic device, and more particularly, to a neuromorphic device including post-synaptic neurons having comparators for deciding and resetting quasi-learned synapses.
2. Description of the Related Art
Recently, much attention has been paid to neuromorphic technology using chips that mimic the human brain. A neuromorphic device used in the neuromorphic technology includes a plurality of pre-synaptic neurons, a plurality of post-synaptic neurons, and a plurality of synapses. The neuromorphic device outputs pulses or spikes depending on a variety of levels, amplitudes, or times, according to learned states.
In a learning mode, untrained synapses, among the synapses of the neuromorphic device, may be quasi-trained. In this case, when a learned data pattern is read out from the neuromorphic device, a data pattern similar to the learned data pattern may be outputted from the quasi-trained synapses. At this time, the neuromorphic device may malfunction. Therefore, the quasi-trained synapses need to be reset to an initial state in which the synapses are not trained.